Prognostic and predictive value of a microRNA signature in stage II colon cancer: a microRNA expression analysis

High expression of PAX8-AS1 correlates with poor prognosis and response to fluorouracil-based chemotherapy in stage II colon cancer

Decisions regarding adjuvant therapy in patients with stage II colon cancer remains controversial and challenging. We aimed to determine novel biomarkers that help to predict relapse free survival (RFS) and identify a subset of patients with stage II colon cancer who could gain survival benefits from adjuvant chemotherapy. Public microarray datasets of stage II colon cancer samples were extracted from Gene Expression Omnibus database. Global gene expression changes were then analyzed between the paired early relapse and long-term survival group to identify the differentially expressed mRNAs and lncRNAs. Based on Lasso Cox regression modeling analysis, a total of 30 mRNAs and 2 lncRNAs were finally identified. With specific formula, stage II patients in training and validation sets were divided into low and risk groups with significantly different RFS. PAX8-AS1 is the novel lncRNA which showed the highest upregulation in early relapse group. Patients with high PAX8-AS1 expression level showed notably poorer RFS in both meta GEO cohort (P = 0.04, Figure 4B) and FUSCC cohort (P < 0.001, Figure 4C). Among the stage II patients with high PAX8-AS1 level, administration of fluorouracil-based adjuvant chemotherapy provided a substantial improvement in RFS (P = 0.002, Figure 3C). Further mechanistic study unveiled that PAX8-AS1 increases the response of CRC cells to chemotherapy in vitro and in vivo by maintaining the mRNA stability of PAX8. In conclusion, PAX8-AS1 as a novel and reliable biomarker for predicting prognosis and identification of patients with stage II disease who could gain survival benefit from fluorouracil-based adjuvant chemotherapy.

The AC010247.2/miR-125b-5p axis triggers the malignant progression of acute myelocytic leukemia by IL-6R

AML is a malignant tumor derived from the hematopoietic system, which has a poor prognosis and its incidence is increasing recent years. LncRNAs bind to miRNAs as competitive endogenous RNAs to regulate the occurrence and progression of AML， with IL-6R playing a crucial role in hematological malignancies. However, the mechanism by which noncoding RNAs regulate IL6R expression in AML remains unclear. This study found that the AC010247.2/miR-125b-5p axis promotes AML progression by regulating IL-6R expression. Specifically, knocking down or inhibiting AC010247.2 and miR-125b-5p affected IL6R and its downstream genes. Mechanistically, AC010247.2 acts as a ceRNA for miR-125b-5p, influencing IL-6R expression. Additionally, AC010247.2's regulation of AML progression partially depends on miR-125b-5p. Notably, the AC010247.2/miR-125b-5p/IL6R axis serves as a better polygenic diagnostic marker for AML. Our study identifies a key ceRNA regulatory axis that modulates IL6R expression in AML, providing a reliable multigene diagnostic method and potential therapeutic target.

The miRNA and PD-1/PD-L1 signaling axis: an arsenal of immunotherapeutic targets against lung cancer

Lung cancer is a severe challenge to the health care system with intrinsic resistance to first and second-line chemo/radiotherapies. In view of the sterile environment of lung cancer, several immunotherapeutic drugs including nivolumab, pembrolizumab, atezolizumab, and durvalumab are currently being used in clinics globally with the intention of releasing exhausted T-cells back against refractory tumor cells. Immunotherapies have a limited response rate and may cause immune-related adverse events (irAEs) in some patients. Hence, a deeper understanding of regulating immune checkpoint interactions could significantly enhance lung cancer treatments. In this review, we explore the role of miRNAs in modulating immunogenic responses against tumors. We discuss various aspects of how manipulating these checkpoints can bias the immune system's response against lung cancer. Specifically, we examine how altering the miRNA profile can impact the activity of various immune checkpoint inhibitors, focusing on the PD-1/PD-L1 pathway within the complex landscape of lung cancer. We believe that a clear understanding of the host's miRNA profile can influence the efficacy of checkpoint inhibitors and significantly contribute to existing immunotherapies for lung cancer patients. Additionally, we discuss ongoing clinical trials involving immunotherapeutic drugs, both as standalone treatments and in combination with other therapies, intending to advance the development of immunotherapy for lung cancer.

A ferroptosis-associated prognostic model correlated with immune landscape and radiotherapy response in low-grade gliomas (LGGs)

Despite receiving comprehensive treatment, the prognosis for low-grade gliomas (LGGs) patients varies considerably. Recent studies have focused extensively on ferroptosis, across a range of tumor types. Nevertheless, methodologies to evaluate the efficacy of radiotherapy for LGGs, from the perspective of ferroptosis-related genes (FRGs), remain strikingly rare. In this study, we conducted a retrospective study on the transcriptional profiles of LGG patients from the public databases and a local cohort. An FRG model was developed and validated, exhibits heightened robustness when contrasted with the traditional ssGSEA model. Patients demonstrating higher FRG scores were identified as a high-risk group, displaying a worse prognosis. By incorporating the FRG score alongside other prognosis-associated clinical indicators, we formulated an enhanced nomogram to achieve a higher level of prediction performance. Additionally, among LGG patients receiving radiotherapy, a poorer prognosis was observed in the high-risk group. Further investigation revealed that samples from the high-risk group generally exhibit a TME in an immuno-suppressive state. Collectively, we developed an FRG model and a robust nomogram for LGG prognostication. This study suggests that a high FRG score, indicative of an immunosuppressive TME, could potentially lead to a less favorable prognosis for certain LGG patients receiving radiotherapy.

Predicting the risk of glaucoma-related adverse events following secondary intraocular lens implantation in paediatric eyes: a 3-year study

AIMS

To establish and evaluate predictive models for glaucoma-related adverse events (GRAEs) following secondary intraocular lens (IOL) implantation in paediatric eyes.

METHODS

205 children (356 aphakic eyes) receiving secondary IOL implantation at Zhongshan Ophthalmic Center with a 3-year follow-up were enrolled. Cox proportional hazard model was used to identify predictors of GRAEs and developed nomograms. Model performance was evaluated with time-dependent receiver operating characteristic (ROC) curves, decision curve analysis, Kaplan-Meier curves and validated internally through C-statistics and calibration plot of the bootstrap samples.

RESULTS

Older age at secondary IOL implantation (HR=1.5, 95% CI: 1.03 to 2.19), transient intraocular hypertension (HR=9.06, 95% CI: 2.97 to 27.67) and ciliary sulcus implantation (HR=14.55, 95% CI: 2.11 to 100.57) were identified as risk factors for GRAEs (all p<0.05). Two nomograms were established. At postoperatively 1, 2 and 3 years, model 1 achieved area under the ROC curves (AUCs) of 0.747 (95% CI: 0.776 to 0.935), 0.765 (95% CI: 0.804 to 0.936) and 0.748 (95% CI: 0.736 to 0.918), and the AUCs of model 2 were 0.881 (95% CI: 0.836 to 0.926), 0.895 (95% CI: 0.852 to 0.938) and 0.848 (95% CI: 0.752 to 0.945). Both models demonstrated fine clinical net benefit and performance in the interval validation. The Kaplan-Meier curves showing two distinct risk groups were well discriminated and robust in both models. An online risk calculator was constructed.

CONCLUSION

Two nomograms could sensitively and accurately identify children at high risk of GRAEs after secondary IOL implantation to help early identification and timely intervention.

Risk Without Reward: Differing Patterns of Chemotherapy Use Do Not Improve Outcomes in Stage II Early-Onset Colon Cancer

PURPOSE

Rising rates of early-onset colon cancer (EOCC) present challenges in deciding how to optimally treat patients. Although standard of care for stage II CC is surgical resection, adding chemotherapy for high-risk disease, evidence suggests treatment selection may differ by age. We investigated whether adjuvant chemotherapy (AC) administration rates differ between patients with early- and later-onset stage II CC.

METHODS

Data originated from the nationwide Flatiron Health electronic health record (EHR)-derived deidentified database spanning January 1, 2003, to August 1, 2021. Adults with stage II CC were grouped as age 18-49 years (EOCC) and those age 50 years or older (later-onset colon cancer [LOCC]). Demographics, Eastern Cooperative Oncology Group score, tumor stage and site, and chemotherapy were included. Primary outcomes included rates of AC administration by age and ethnicity; secondary outcomes included overall survival (OS) and time to metastatic disease (TTMD). Univariate and multivariable logistic regression models evaluated relationships between chemotherapy administration, age, and ethnicity, adjusting for significant covariates.

RESULTS

One thousand sixty-five patients were included. Median age of patients with EOCC was 45.0 years versus 69.0 years for patients with LOCC. Adjusted multivariate analysis showed patients with EOCC received AC significantly more often than patients with LOCC. Non-Hispanic patients received AC at significantly lower rates than Hispanic patients in both cohorts. Subanalysis of stage IIA patients showed that patients with EOCC were more likely to receive AC than patients with LOCC. No significant differences in OS or TTMD were observed by age regardless of AC administration in stage II overall; however, patients with stage IIA EOCC receiving AC had significantly longer TTMD than those not receiving AC.

CONCLUSION

AC was given preferentially in stage II EOCC, even in stage IIA, despite deviation from guidelines. This may expose low-risk patients to unnecessary toxicities and suggests bias toward treating younger patients more aggressively, despite unclear evidence for better outcomes.

Multiomics sequencing and immune microenvironment characteristics define three subtypes of small cell neuroendocrine carcinoma of the cervix

Small cell cervical carcinoma (SCCC) is the most common neuroendocrine tumor in the female genital tract, with an unfavorable prognosis and lacking an evidence-based therapeutic approach. Until now, the distinct subtypes and immune characteristics of SCCC combined with genome and transcriptome have not been described. We performed genomic (n = 18), HPV integration (n = 18), and transcriptomic sequencing (n = 19) of SCCC samples. We assessed differences in immune characteristics between SCCC and conventional cervical cancer, and other small cell neuroendocrine carcinomas, through bioinformatics analysis and immunohistochemical assays. We stratified SCCC patients through non-negative matrix factorization and described the characteristics of these distinct types. We further validated it using multiplex immunofluorescence (n = 77) and investigated its clinical prognostic effect. We confirmed a high frequency of PIK3CA and TP53 alterations and HPV18 integrations in SCCC. SCCC and other small cell carcinoma had similar expression signatures and immune cell infiltration patterns. Comparing patients with SCCC to those with conventional cervical cancer, the former presented immune excluded or 'desert' infiltration. The number of CD8+ cells in the invasion margin of SCCC patients predicted favorable clinical outcomes. We identified three transcriptome subtypes: an inflamed phenotype with high-level expression of genes related to the MHC-II complex (CD74) and IFN-α/β (SCCC-I), and two neuroendocrine subtypes with high-level expression of ASCL1 or NEUROD1, respectively. Combined with multiple technologies, we found that the neuroendocrine groups had more TP53 mutations and SCCC-I had more PIK3CA mutations. Multiplex immunofluorescence validated these subtypes and SCCC-I was an independent prognostic factor of overall survival. These results provide insights into SCCC tumor heterogeneity and potential therapies. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Lnc-216 regulates the miR-143-5p /MMP2 signaling axis aggravates retinal endothelial cell dysfunction

PURPOSE

Diabetic retinopathy (DR) is a serious retinal vascular disease that affects many individuals in their prime working years. The present research aimed at whether and how LOC681216 (LNC-216) is involved in retinal vascular dysfunction under diabetic conditions.

METHODS

Rat retinal microvascular endothelial cells (RRMECs) treated with high glucose (HG) were used for functional analysis. Gene expression analysis was conducted using the Clariom D Affymetrix platform. The wound healing, transwell, and vascular tube formation assays were used to identify the migration, invasion, and tube formation capability of RRMECs. The dual-luciferase reporter confirmed the binding interaction between miR-143-5p and LNC-216 or matrix metallopeptidase 2 (MMP2).

RESULTS

Lnc-216 was upregulated in RRMECs treated with HG. Lnc-216 knockdown markedly suppressed the tube formation, cell migration, and wound healing of cultured RRMECs under HG conditions. Mechanistically, Lnc-216 acted as a miR-143-5p sponge to affect the biological activity of miR-143-5p, which led to increased expression of matrix metallopeptidase 2 (MMP2).

CONCLUSIONS

Lnc-216 attenuates diabetic retinal vascular dysfunction through the miR-143-5p/MMP2 axis, providing a potential therapeutic strategy for DR.

Circulating miRNA Signature Predicts Cancer Incidence in Lynch Syndrome-A Pilot Study

Lynch syndrome (LS) is the most common autosomal dominant cancer syndrome and is characterized by high genetic cancer risk modified by lifestyle factors. This study explored whether a circulating miRNA (c-miR) signature predicts LS cancer incidence within a 4-year prospective surveillance period. To gain insight how lifestyle behavior could affect LS cancer risk, we investigated whether the cancer-predicting c-miR signature correlates with known risk-reducing factors such as physical activity, body mass index (BMI), dietary fiber, or NSAID usage. The study included 110 c-miR samples from LS carriers, 18 of whom were diagnosed with cancer during a 4-year prospective surveillance period. Lasso regression was utilized to find c-miRs associated with cancer risk. Individual risk sum derived from the chosen c-miRs was used to develop a model to predict LS cancer incidence. This model was validated using 5-fold cross-validation. Correlation and pathway analyses were applied to inspect biological functions of c-miRs. Pearson correlation was used to examine the associations of c-miR risk sum and lifestyle factors. hsa-miR-10b-5p, hsa-miR-125b-5p, hsa-miR-200a-3p, hsa-miR-3613-5p, and hsa-miR-3615 were identified as cancer predictors by Lasso, and their risk sum score associated with higher likelihood of cancer incidence (HR 2.72, 95% confidence interval: 1.64-4.52, C-index = 0.72). In cross-validation, the model indicated good concordance with the average C-index of 0.75 (0.6-1.0). Coregulated hsa-miR-10b-5p, hsa-miR-125b-5p, and hsa-miR-200a-3p targeted genes involved in cancer-associated biological pathways. The c-miR risk sum score correlated with BMI (r = 0.23, P < 0.01). In summary, BMI-associated c-miRs predict LS cancer incidence within 4 years, although further validation is required.

PREVENTION RELEVANCE

The development of cancer risk prediction models is key to improving the survival of patients with LS. This pilot study describes a serum miRNA signature-based risk prediction model that predicts LS cancer incidence within 4 years, although further validation is required.

Effect of chemotherapy on prognosis in patients with primary pancreatic signet ring cell carcinoma: A large real-world study based on machine learning

BACKGROUND

Primary pancreatic signet ring cell carcinoma (PSRCC), an extremely rare histologic variant of pancreatic cancer, has a poor prognosis. This study aimed to investigate the prognostic value of chemotherapy in PSRCC.

METHODS

Patients with PSRCC between 2000 and 2019 were identified using the Surveillance Epidemiology and End Results (SEER) database. The main outcomes in this study were cancer-specific survival (CSS) and overall survival (OS). The baseline characteristics of patients were compared using Pearson's Chi-square test. Kaplan-Meier analysis was used to generate the survival curves. Least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression models, and Random Survival Forest model were used to analyze the prognostic variables for OS and CSS. The variance inflation factors (VIFs) were used to analyze whether there was an overfitting problem.

RESULTS

A total of 588 patients were identified. Chemotherapy was an independent prognostic factor for OS and CSS, and significantly associated with OS (HR = 0.33, 95% CI = 0.27-0.40, P <0.001) and CSS (HR = 0.32, 95% CI = 0.26-0.39, P <0.001).

CONCLUSIONS

Chemotherapy showed beneficial effects on OS and CSS in patients with PSRCC and should be recommended in clinical practice.

Predicting 5-year recurrence risk in colorectal cancer: development and validation of a histology-based deep learning approach

BACKGROUND

Accurate estimation of the long-term risk of recurrence in patients with non-metastatic colorectal cancer (CRC) is crucial for clinical management. Histology-based deep learning is expected to provide more abundant information for risk stratification.

METHODS

We developed and validated a weakly supervised deep-learning model for predicting 5-year relapse-free survival (RFS) to stratify patients with different risks based on histological images from three hospitals of 614 cases with non-metastatic CRC. A deep prognostic factor (DL-RRS) was established to stratify patients into high and low-risk group. The areas under the curve (AUCs) were calculated to evaluate the performances of models.

RESULTS

Our proposed model achieves the AUCs of 0.833 (95% CI: 0.736-0.905) and 0.715 (95% CI: 0.647-0.776) on validation cohort and external test cohort, respectively. The 5-year RFS rate was 45.7% for high DL-RRS patients, and 82.5% for low DL-RRS patients respectively in the external test cohort (HR: 3.89, 95% CI: 2.51-6.03, P < 0.001). Adjuvant chemotherapy was associated with improved RFS in Stage II patients with high DL-RRS (HR: 0.15, 95% CI: 0.06-0.38, P < 0.001).

CONCLUSIONS

DL-RRS has a good predictive performance of 5-year recurrence risk in CRC, and will better serve the clinical decision-making.

MicroRNA Sensors Based on CRISPR/Cas12a Technologies: Evolution From Indirect to Direct Detection

MicroRNA (miRNA) has emerged as a promising biomarker for disease diagnosis and a potential therapeutic targets for drug development. The detection of miRNA can serve as a noninvasive tool in diseases diagnosis and predicting diseases prognosis. CRISPR/Cas12a system has great potential in nucleic acid detection due to its high sensitivity and specificity, which has been developed to be a versatile tool for nucleic acid-based detection of targets in various fields. However, conversion from RNA to DNA with or without amplification operation is necessary for miRNA detection based on CRISPR/Cas12a system, because dsDNA containing PAM sequence or ssDNA is traditionally considered as the activator of Cas12a. Until recently, direct detection of miRNA by CRISPR/Cas12a system has been reported. In this review, we provide an overview of the evolution of biosensors based on CRISPR/Cas12a for miRNA detection from indirect to direct, which would be beneficial to the development of CRISPR/Cas12a-based sensors with better performance for direct detection of miRNA.

Radiomics and dosiomics for predicting complete response to definitive chemoradiotherapy patients with oesophageal squamous cell cancer using the hybrid institution model

OBJECTIVES

To develop a multi-institutional prediction model to estimate the local response to oesophageal squamous cell carcinoma (ESCC) treated with definitive radiotherapy based on radiomics and dosiomics features.

METHODS

The local responses were categorised into two groups (incomplete and complete). An external validation model and a hybrid model that the patients from two institutions were mixed randomly were proposed. The ESCC patients at stages I-IV who underwent chemoradiotherapy from 2012 to 2017 and had follow-up duration of more than 5 years were included. The patients who received palliative or pre-operable radiotherapy and had no FDG PET images were excluded. The segmentations included the GTV, CTV, and PTV which are used in treatment planning. In addition, shrinkage, expansion, and shell regions were created. Radiomic and dosiomic features were extracted from CT, FDG PET images, and dose distribution. Machine learning-based prediction models were developed using decision tree, support vector machine, k-nearest neighbour (kNN) algorithm, and neural network (NN) classifiers.

RESULTS

A total of 116 and 26 patients enrolled at Centre 1 and Centre 2, respectively. The external validation model exhibited the highest accuracy with 65.4% for CT-based radiomics, 77.9% for PET-based radiomics, and 72.1% for dosiomics based on the NN classifiers. The hybrid model exhibited the highest accuracy of 84.4% for CT-based radiomics based on the kNN classifier, 86.0% for PET-based radiomics, and 79.0% for dosiomics based on the NN classifiers.

CONCLUSION

The proposed hybrid model exhibited promising predictive performance for the local response to definitive radiotherapy in ESCC patients.

CLINICAL RELEVANCE STATEMENT

The prediction of the complete response for oesophageal cancer patients may contribute to improving overall survival. The hybrid model has the potential to improve prediction performance than the external validation model that was conventionally proposed.

KEY POINTS

• Radiomics and dosiomics used to predict response in patients with oesophageal cancer receiving definitive radiotherapy. • Hybrid model with neural network classifier of PET-based radiomics improved prediction accuracy by 8.1%. • The hybrid model has the potential to improve prediction performance.

microRNA-622 upregulates cell cycle process by targeting FOLR2 to promote CRC proliferation

BACKGROUND

Epigenetic alterations contribute greatly to the development and progression of colorectal cancer, and effect of aberrant miR-622 expression is still controversial. This study aimed to discover miR-622 regulation in CRC proliferation.

METHODS

miR-622 expression and prognosis were analyzed in clinical CRC samples from Nanfang Hospital. miR-622 regulation on cell cycle and tumor proliferation was discovered, and FOLR2 was screened as functional target of miR-622 using bioinformatics analysis, which was validated via dual luciferase assay and gain-of-function and loss-of-function experiments both in vitro and in vivo.

RESULTS

miR-622 overexpression in CRC indicated unfavorable prognosis and it regulated cell cycle to promote tumor growth both in vitro and in vivo. FOLR2 is a specific, functional target of miR-622, which negatively correlates with signature genes in cell cycle process to promote CRC proliferation.

CONCLUSIONS

miR-622 upregulates cell cycle process by targeting FOLR2 to promote CRC proliferation, proposing a novel mechanism and treatment target in CRC epigenetic regulation of miR-622.

Prognostic Implications of LRP1B and Its Relationship with the Tumor-Infiltrating Immune Cells in Gastric Cancer

BACKGROUND

Recent studies have shown that low-density lipoprotein receptor-related protein 1b (LRP1B), as a potential tumor suppressor, is implicated in the response to immunotherapy. The frequency of LRP1B mutation gene is high in many cancers, but its role in gastric cancer (GC) has not been determined.

METHODS

The prognostic value of LRP1B mutation in a cohort containing 100 patients having received radical gastrectomy for stage II-III GC was explored. By analyzing the data of LRP1B mRNA, the risk score of differentially expressed genes (DEGs) between LRP1B mutation-type and wild-type was constructed based on the TCGA-STAD cohort. The infiltration of tumor immune cells was evaluated by the CYBERSORT algorithm and verified by immunohistochemistry.

RESULTS

LRP1B gene mutation was an independent risk factor for disease-free survival (DFS) in GC patients (HR = 2.57, 95% CI: 1.28-5.14, p = 0.008). The Kaplan-Meier curve demonstrated a shorter survival time in high-risk patients stratified according to risk score (p < 0.0001). CYBERSORT analysis showed that the DEGs were mainly concentrated in CD4+ T cells and macrophages. TIMER analysis suggested that LRP1B expression was associated with the infiltration of CD4+ T cells and macrophages. Immunohistochemistry demonstrated that LRP1B was expressed in the tumor cells (TCs) and immune cells in 16/89 and 26/89 of the cohort, respectively. LRP1B-positive TCs were associated with higher levels of CD4+ T cells, CD8+ T cells, and CD86/CD163 (p < 0.05). Multivariate analysis showed that LRP1B-positive TCs represented an independent protective factor of DFS in GC patients (HR = 0.43, 95% CI: 0.10-0.93, p = 0.042).

CONCLUSIONS

LRP1B has a high prognostic value in GC. LRP1B may stimulate tumor immune cell infiltration to provide GC patients with survival benefits.

Development and validation of a novel radiomics nomogram for prediction of early recurrence in colorectal cancer

BACKGROUND

Early recurrence (ER) is a significant concern following curative resection of advanced colorectal cancer (CRC) and is linked to poor long-term survival. Reliable prediction of ER is challenging, necessitating the development of a novel radiomics-based nomogram for CRC patients.

METHODS

We enrolled 405 patients, with 298 in the training set and 107 in the external test set. Radiomic features were extracted from preoperative venous-phase computed tomography (CT) images. A radiomics signature was created using univariate logistic regression analyses and the least absolute shrinkage and selection operator algorithm. Clinical factors were integrated into the analyses to develop a comprehensive predictive tool in a multivariate logistic regression model, resulting in a radiomics nomogram. Subsequently, the calibration, discrimination, and clinical usefulness of the nomogram were evaluated.

RESULTS

The radiomics signature, consisting of four selected CT features, was significantly associated with ER in both the training and test datasets (P < 0.05). Independent predictors of ER included TNM stage, carcinoembryonic antigen level and differentiation grade were identified. The radiomics nomogram, incorporating all these predictors, exhibited good predictive ability in both the training set with an area under the curve (AUC) of 0.82 (95 % confidence interval (CI), 0.74-0.90) and the test set with an AUC of 0.85 (95 % CI, 0.72-0.99), surpassing the performance of any single candidate factor alone. Furthermore, additional analysis demonstrated that the nomogram was clinically useful.

CONCLUSIONS

We have developed a radiomics-based nomogram that effectively predicts early recurrence in CRC patients, enhancing the potential for timely intervention and improved outcomes.

DAEMDA: A Method with Dual-Channel Attention Encoding for miRNA-Disease Association Prediction

A growing number of studies have shown that aberrant microRNA (miRNA) expression is closely associated with the evolution and development of various complex human diseases. These key biomarkers' identification and observation are significant for gaining a deeper understanding of disease pathogenesis and therapeutic mechanisms. Consequently, pinpointing potential miRNA-disease associations (MDA) has become a prominent bioinformatics subject, encouraging several new computational methods given the advances in graph neural networks (GNN). Nevertheless, these existing methods commonly fail to exploit the network nodes' global feature information, leaving the generation of high-quality embedding representations using graph properties as a critical unsolved issue. Addressing these challenges, we introduce the DAEMDA, a computational method designed to optimize the current models' efficacy. First, we construct similarity and heterogeneous networks involving miRNAs and diseases, relying on experimentally corroborated miRNA-disease association data and analogous information. Then, a newly-fashioned parallel dual-channel feature encoder, designed to better comprehend the global information within the heterogeneous network and generate varying embedding representations, follows this. Ultimately, employing a neural network classifier, we merge the dual-channel embedding representations and undertake association predictions between miRNA and disease nodes. The experimental results of five-fold cross-validation and case studies of major diseases based on the HMDD v3.2 database show that this method can generate high-quality embedded representations and effectively improve the accuracy of MDA prediction.

Identification of the shared gene signature and biological mechanism between type 2 diabetes and colorectal cancer

Background: The correlation of type 2 diabetes mellitus (T2DM) with colorectal cancer (CRC) has garnered considerable attention in the scientific community. Despite this, the molecular mechanisms underlying the interaction between these two diseases are yet to be elucidated. Hence, the present investigation aims to explore the shared gene signatures, immune profiles, and drug sensitivity patterns that exist between CRC and T2DM. Methods: RNA sequences and characteristics of patients with CRC and T2DM were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. These were investigated using weighted gene co-expression network analysis (WGCNA) to determine the co-expression networks linked to the conditions. Genes shared between CRC and T2DM were analyzed by univariate regression, followed by risk prognosis assessment using the LASSO regression model. Various parameters were assessed through different software such as the ESTIMATE, CIBERSORT, AND SSGSEA utilized for tumor immune infiltration assessment in the high- and low-risk groups. Additionally, pRRophetic was utilized to assess the sensitivity to chemotherapeutic agents in both groups. This was followed by diagnostic modeling using logistic modeling and clinical prediction modeling using the nomogram. Results: WGCNA recognized four and five modules that displayed a high correlation with T2DM and CRC, respectively. In total, 868 genes were shared between CRC and T2DM, with 14 key shared genes being identified in the follow-up analysis. The overall survival (OS) of patients in the low-risk group was better than that of patients in the high-risk group. In contrast, the high-risk group exhibited higher expression levels of immune checkpoints The Cox regression analyses established that the risk-score model possessed independent prognostic value in predicting OS. To facilitate the prediction of OS and cause-specific survival, the nomogram was established utilizing the Cox regression model. Conclusion: The T2DM + CRC risk-score model enabled independent prediction of OS in individuals with CRC. Moreover, these findings revealed novel genes that hold promise as therapeutic targets or biomarkers in clinical settings.

Role of non-coding RNAs as new therapeutic targets in regulating the EMT and apoptosis in metastatic gastric and colorectal cancers

Colorectal cancer (CRC) and gastric cancer (GC), are the two most common cancers of the gastrointestinal tract, and are serious health concerns worldwide. The discovery of more effective biomarkers for early diagnosis, and improved patient prognosis is important. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), can regulate cellular processes such as apoptosis and the epithelial-mesenchymal transition (EMT) leading to progression and resistance of GC and CRC tumors. Moreover these pathways (apoptosis and EMT) may serve as therapeutic targets, to prevent metastasis, and to overcome drug resistance. A subgroup of ncRNAs is common to both GC and CRC tumors, suggesting that they might be used as biomarkers or therapeutic targets. In this review, we highlight some ncRNAs that can regulate EMT and apoptosis as two opposite mechanisms in cancer progression and metastasis in GC and CRC. A better understanding of the biological role of ncRNAs could open up new avenues for the development of personalized treatment plans for GC and CRC patients.

Exploration of the radiosensitivity-related prognostic risk signature in patients with glioma: evidence from microarray data

BACKGROUND

Gene expression signatures can be used as prognostic biomarkers in various types of cancers. We aim to develop a gene signature for predicting the response to radiotherapy in glioma patients.

METHODS

Radio-sensitive and radio-resistant glioma cell lines (M059J and M059K) were subjected to microarray analysis to screen for differentially expressed mRNAs. Additionally, we obtained 169 glioblastomas (GBM) samples and 5 normal samples from The Cancer Genome Atlas (TCGA) database, as well as 80 GBM samples and 4 normal samples from the GSE7696 set. The "DESeq2" R package was employed to identify differentially expressed genes (DEGs) between the normal brain samples and GBM samples. Combining the prognostic-related molecules identified from the TCGA, we developed a radiosensitivity-related prognostic risk signature (RRPRS) in the training set, which includes 152 patients with glioblastoma. Subsequently, we validated the reliability of the RRPRS in a validation set containing 616 patients with glioma from the TCGA database, as well as an internal validation set consisting of 31 glioblastoma patients from the Nanfang Hospital, Southern Medical University.

RESULTS

Based on the microarray and LASSO COX regression analysis, we developed a nine-gene radiosensitivity-related prognostic risk signature. Patients with glioma were divided into high- or low-risk groups based on the median risk score. The Kaplan-Meier survival analysis showed that the progression-free survival (PFS) of the high-risk group was significantly shorter. The signature accurately predicted PFS as assessed by time-dependent receiver operating characteristic curve (ROC) analyses. Stratified analysis demonstrated that the signature is specific to predict the outcome of patients who were treated using radiotherapy. Univariate and multivariate Cox regression analysis revealed that the predictor was an independent predictor for the prognosis of patients with glioma. The prognostic nomograms accompanied by calibration curves displayed the 1-, 2-, and 3-year PFS and OS in patients with glioma.

CONCLUSION

Our study established a new nine-gene radiosensitivity-related prognostic risk signature that can predict the prognosis of patients with glioma who received radiotherapy. The nomogram showed great potential to predict the prognosis of patients with glioma treated using radiotherapy.

The pathological role of C-X-C chemokine receptor type 4 (CXCR4) in colorectal cancer (CRC) progression; special focus on molecular mechanisms and possible therapeutics

Colorectal cancer (CRC) is comprised of transformed cells and non-malignant cells including cancer-associated fibroblasts (CAF), endothelial vasculature cells, and tumor-infiltrating cells. These nonmalignant cells, as well as soluble factors (e.g., cytokines), and the extracellular matrix (ECM), form the tumor microenvironment (TME). In general, the cancer cells and their surrounding TME can crosstalk by direct cell-to-cell contact and via soluble factors, such as cytokines (e.g., chemokines). TME not only promotes cancer progression through growth-promoting cytokines but also provides resistance to chemotherapy. Understanding the mechanisms of tumor growth and progression and the roles of chemokines in CRC will likely suggest new therapeutic targets. In this line, a plethora of reports has evidenced the critical role of chemokine receptor type 4 (CXCR4)/C-X-C motif chemokine ligand 12 (CXCL12 or SDF-1) axis in CRC pathogenesis. In the current review, we take a glimpse into the role of the CXCR4/CXCL12 axis in CRC growth, metastasis, angiogenesis, drug resistance, and immune escape. Also, a summary of recent reports concerning targeting CXCR4/CXCL12 axis for CRC management and therapy has been delivered.

Development and validation of prediction models for the prognosis of colon cancer with lung metastases: a population-based cohort study

Background

Current studies on the establishment of prognostic models for colon cancer with lung metastasis (CCLM) were lacking. This study aimed to construct and validate prediction models of overall survival (OS) and cancer-specific survival (CSS) probability in CCLM patients.

Method

Data on 1,284 patients with CCLM were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly assigned with 7:3 (stratified by survival time) to a development set and a validation set on the basis of computer-calculated random numbers. After screening the predictors by the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression, the suitable predictors were entered into Cox proportional hazard models to build prediction models. Calibration curves, concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were used to perform the validation of models. Based on model-predicted risk scores, patients were divided into low-risk and high-risk groups. The Kaplan-Meier (K-M) plots and log-rank test were applied to perform survival analysis between the two groups.

Results

Building upon the LASSO and multivariate Cox regression, six variables were significantly associated with OS and CSS (i.e., tumor grade, AJCC T stage, AJCC N stage, chemotherapy, CEA, liver metastasis). In development, validation, and expanded testing sets, AUCs and C-indexes of the OS and CSS prediction models were all greater than or near 0.7, which indicated excellent predictability of models. On the whole, the calibration curves coincided with the diagonal in two models. DCA indicated that the models had higher clinical benefit than any single risk factor. Survival analysis results showed that the prognosis was worse in the high-risk group than in the low-risk group, which suggested that the models had significant discrimination for patients with different prognoses.

Conclusion

After verification, our prediction models of CCLM are reliable and can predict the OS and CSS of CCLM patients in the next 1, 3, and 5 years, providing valuable guidance for clinical prognosis estimation and individualized administration of patients with CCLM.

miR-223: a key regulator of pulmonary inflammation

Small noncoding RNAs, known as microRNAs (miRNAs), are vital for the regulation of diverse biological processes. miR-223, an evolutionarily conserved anti-inflammatory miRNA expressed in cells of the myeloid lineage, has been implicated in the regulation of monocyte-macrophage differentiation, proinflammatory responses, and the recruitment of neutrophils. The biological functions of this gene are regulated by its expression levels in cells or tissues. In this review, we first outline the regulatory role of miR-223 in granulocytes, macrophages, endothelial cells, epithelial cells and dendritic cells (DCs). Then, we summarize the possible role of miR-223 in chronic obstructive pulmonary disease (COPD), acute lung injury (ALI), coronavirus disease 2019 (COVID-19) and other pulmonary inflammatory diseases to better understand the molecular regulatory networks in pulmonary inflammatory diseases.

LncRNA miR663AHG represses the development of colon cancer in a miR663a-dependent manner

The MIR663AHG gene encodes both miR663AHG and miR663a. While miR663a contributes to the defense of host cells against inflammation and inhibits colon cancer development, the biological function of lncRNA miR663AHG has not been previously reported. In this study, the subcellular localization of lncRNA miR663AHG was determined by RNA-FISH. miR663AHG and miR663a were measured by qRT-PCR. The effects of miR663AHG on the growth and metastasis of colon cancer cells were investigated in vitro and in vivo. CRISPR/Cas9, RNA pulldown, and other biological assays were used to explore the underlying mechanism of miR663AHG. We found that miR663AHG was mainly distributed in the nucleus of Caco2 and HCT116 cells and the cytoplasm of SW480 cells. The expression level of miR663AHG was positively correlated with the level of miR663a (r = 0.179, P = 0.015) and significantly downregulated in colon cancer tissues relative to paired normal tissues from 119 patients (P < 0.008). Colon cancers with low miR663AHG expression were associated with advanced pTNM stage (P = 0.021), lymph metastasis (P = 0.041), and shorter overall survival (hazard ratio = 2.026; P = 0.021). Experimentally, miR663AHG inhibited colon cancer cell proliferation, migration, and invasion. The growth of xenografts from RKO cells overexpressing miR663AHG was slower than that of xenografts from vector control cells in BALB/c nude mice (P = 0.007). Interestingly, either RNA-interfering or resveratrol-inducing expression changes of miR663AHG or miR663a can trigger negative feedback regulation of transcription of the MIR663AHG gene. Mechanistically, miR663AHG could bind to miR663a and its precursor pre-miR663a, and prevent the degradation of miR663a target mRNAs. Disruption of the negative feedback by knockout of the MIR663AHG promoter, exon-1, and pri-miR663A-coding sequence entirely blocked these effects of miR663AHG, which was restored in cells transfected with miR663a expression vector in rescue experiment. In conclusion, miR663AHG functions as a tumor suppressor that inhibits the development of colon cancer through its cis-binding to miR663a/pre-miR663a. The cross talk between miR663AHG and miR663a expression may play dominant roles in maintaining the functions of miR663AHG in colon cancer development.

Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation study

BACKGROUND

Improved markers for predicting recurrence are needed to stratify patients with localised (stage I-III) renal cell carcinoma after surgery for selection of adjuvant therapy. We developed a novel assay integrating three modalities-clinical, genomic, and histopathological-to improve the predictive accuracy for localised renal cell carcinoma recurrence.

METHODS

In this retrospective analysis and validation study, we developed a histopathological whole-slide image (WSI)-based score using deep learning allied to digital scanning of conventional haematoxylin and eosin-stained tumour tissue sections, to predict tumour recurrence in a development dataset of 651 patients with distinctly good or poor disease outcome. The six single nucleotide polymorphism-based score, which was detected in paraffin-embedded tumour tissue samples, and the Leibovich score, which was established using clinicopathological risk factors, were combined with the WSI-based score to construct a multimodal recurrence score in the training dataset of 1125 patients. The multimodal recurrence score was validated in 1625 patients from the independent validation dataset and 418 patients from The Cancer Genome Atlas set. The primary outcome measured was the recurrence-free interval (RFI).

FINDINGS

The multimodal recurrence score had significantly higher predictive accuracy than the three single-modal scores and clinicopathological risk factors, and it precisely predicted the RFI of patients in the training and two validation datasets (areas under the curve at 5 years: 0·825-0·876 vs 0·608-0·793; p<0·05). The RFI of patients with low stage or grade is usually better than that of patients with high stage or grade; however, the RFI in the multimodal recurrence score-defined high-risk stage I and II group was shorter than in the low-risk stage III group (hazard ratio [HR] 4·57, 95% CI 2·49-8·40; p<0·0001), and the RFI of the high-risk grade 1 and 2 group was shorter than in the low-risk grade 3 and 4 group (HR 4·58, 3·19-6·59; p<0·0001).

INTERPRETATION

Our multimodal recurrence score is a practical and reliable predictor that can add value to the current staging system for predicting localised renal cell carcinoma recurrence after surgery, and this combined approach more precisely informs treatment decisions about adjuvant therapy.

FUNDING

National Natural Science Foundation of China, and National Key Research and Development Program of China.

Identification of a Twelve-microRNA Signature with Prognostic Value in Stage II Microsatellite Stable Colon Cancer

We aimed to identify and validate a set of miRNAs that could serve as a prognostic signature useful to determine the recurrence risk for patients with COAD. Small RNAs from tumors of 100 stage II, untreated, MSS colon cancer patients were sequenced for the discovery step. For this purpose, we built an miRNA score using an elastic net Cox regression model based on the disease-free survival status. Patients were grouped into high or low recurrence risk categories based on the median value of the score. We then validated these results in an independent sample of stage II microsatellite stable tumor tissues, with a hazard ratio of 3.24, (CI_95% = 1.05-10.0) and a 10-year area under the receiver operating characteristic curve of 0.67. Functional analysis of the miRNAs present in the signature identified key pathways in cancer progression. In conclusion, the proposed signature of 12 miRNAs can contribute to improving the prediction of disease relapse in patients with stage II MSS colorectal cancer, and might be useful in deciding which patients may benefit from adjuvant chemotherapy.

Developing a nomogram for preoperative prediction of cervical cancer lymph node metastasis by multiplex immunofluorescence

BACKGROUND

Most traditional procedures can destroy tissue natural structure, and the information on spatial distribution and temporal distribution of immune milieu in situ would be lost. We aimed to explore the potential mechanism of pelvic lymph node (pLN) metastasis of cervical cancer (CC) by multiplex immunofluorescence (mIF) and construct a nomogram for preoperative prediction of pLN metastasis in patients with CC.

METHODS

Patients (180 IB1-IIA2 CC patients of 2009 FIGO (International Federation of Gynecology and Obstetrics)) were divided into two groups based on pLN status. Tissue microarray (TMA) was prepared and tumor-infiltrating immune markers were assessed by mIF. Multivariable logistic regression analysis and nomogram were used to develop the predicting model.

RESULTS

Multivariable logistic regression analysis constructs a predictive model and the area under the curve (AUC) can reach 0.843. By internal validation with the remaining 40% of cases, a new ROC curve has emerged and the AUC reached 0.888.

CONCLUSIONS

This study presents an immune nomogram, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CC.

Nutritional and immune-related indicators-based Nomogram for predicting overall survival of surgical oral tongue squamous cell carcinoma

Oral tongue squamous cell carcinoma (OTSCC) is one of the most aggressive oral tumors. The aim of this study was to establish a nomogram to predict overall survival (OS) of TSCC patients after surgery. 169 TSCC patients who underwent surgical treatments in the Cancer Hospital of Shantou University Medical College were included. A nomogram based on Cox regression analysis results was established and internally validated using bootstrap resampling method. pTNM stage, age and total protein, immunoglobulin G, factor B and red blood cell count were identified as independent prognostic factors to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion of the nomogram were lower than those of pTNM stage, indicating a better goodness-of-fit of the nomogram for predicting OS. The bootstrap-corrected concordance index of nomogram was higher than that of pTNM stage (0.794 vs. 0.665, p = 0.0008). The nomogram also had a good calibration and improved overall net benefit. Based on the cutoff value obtained from the nomogram, the proposed high-risk group had poorer OS than low-risk group (p < 0.0001). The nomogram based on nutritional and immune-related indicators represents a promising tool for outcome prediction of surgical OTSCC.

Integrative analysis of negatively regulated miRNA-mRNA axes for esophageal squamous cell carcinoma

BACKGROUND

MicroRNAs regulating mRNA expression by targeting at mRNAs is known constructive in tumor occurrence, immune escape, and metastasis.

OBJECTIVE

This research aims at finding negatively regulatory miRNA-mRNA pairs in esophageal squamous cell carcinoma (ESCC).

METHODS

GENE expression data of The Cancer Genome Atlas (TCGA) and GEO database were employed in differently expressed RNA and miRNA (DE-miRNAs/DE-mRNAs) screening. Function analysis was conducted with DAVID-mirPath. MiRNA-mRNA axes were identified by MiRTarBase and TarBase and verified in esophageal specimen by real-time reverse transcription polymerase chain reaction (RT-qPCR). Receiver operation characteristic (ROC) curve and Decision Curve Analysis (DCA) were applied in miRNA-mRNA pairs predictive value estimation. Interactions between miRNA-mRNA regulatory pairs and immune features were analyzed using CIBERSORT.

RESULTS

Combining TCGA database, 4 miRNA and 10 mRNA GEO datasets, totally 26 DE-miRNAs (13 up and 13 down) and 114 DE-mRNAs (64 up and 50 down) were considered significant. MiRTarBase and TarBase identified 37 reverse regulation miRNA-mRNA pairs, 14 of which had been observed in esophageal tissue or cell line. Through analysis of RT-qPCR outcome, miR-106b-5p/KIAA0232 signature was chosen as characteristic pair of ESCC. ROC and DCA verified the predictive value of model containing miRNA-mRNA axis in ESCC. Via affecting mast cells, miR-106b-5p/KIAA0232 may contribute to tumor microenvironment.

CONCLUSIONS

The diagnostic model of miRNA-mRNA pair in ESCC was established. Their complex role in ESCC pathogenesis especially tumor immunity was partly disclosed.

Prognosis and Nomogram Prediction for Patients with Oral Squamous Cell Carcinoma: A Cohort Study

The TNM staging system is often used to predict the prognosis of patients with oral squamous cell carcinoma (OSCC). However, we have found that patients under the same TNM staging may exhibit tremendous differences in survival rates. Therefore, we aimed to investigate the prognosis of postoperative OSCC patients, establish a nomogram survival prediction model, and verify its effectiveness. Operative logs were reviewed for patients who underwent surgical treatment for OSCC at the Peking University School and Hospital of Stomatology. Patient demographic and surgical records were obtained, and they were followed up for overall survival (OS). A total of 432 patients with oral squamous cell carcinoma were included in the study, with a median follow-up time of 47 months. Based on the results of the Cox regression analysis, we constructed and verified the nomogram prediction model, which includes gender, BMI, OPMDs, pain score, SCC grade, and N stage. The C-index value of the 3-year and 5-year prediction models was 0.782 and 0.770, respectively, proving that the model has a certain level of prediction stability. The new nomogram prediction model has potential clinical significance for predicting the postoperative survival of OSCC patients.

A DNA methylation signature for the prediction of tumour recurrence in stage II colorectal cancer

BACKGROUND

A major challenge in stage II colorectal carcinoma is to identify patients with increased risk of recurrence. Biomarkers that distinguish patients with poor prognosis from patients without recurrence are currently lacking. This study aims to develop a robust DNA methylation classifier that allows the prediction of recurrence and chemotherapy benefit in patients with stage II colorectal cancer. We performed a genome-wide DNA methylation capture sequencing in 243 stage II colorectal carcinoma samples and identified a relapse-specific DNA methylation signature consisting of eight CpG sites.

METHODS

Two hundred and forty-three patients with stage II CRC were enrolled in this study. In order to select differential methylation sites among recurrence and non-recurrence stage II CRC samples, DNA methylation profiles of 62 tumour samples including 31 recurrence and 31 nonrecurrence samples were analysed using the Agilent SureSelectXT Human Methyl-Seq, a comprehensive target enrichment system to analyse CpG methylation. Pyrosequencing was applied to quantify the methylation level of candidate DNA methylation sites in 243 patients. Least absolute shrinkage and selection operator (LASSO) method was employed to build the disease recurrence prediction classifier.

RESULTS

We identified a relapse-related DNA methylation signature consisting of eight CpG sites in stage II CRC by DNA methylation capture sequencing. The classifier showed significantly higher prognostic accuracy than any clinicopathological risk factors. The Kaplan-Meier survival curve showed an association of high-risk score with poor prognosis. In multivariate analysis, the signature was the most significant prognosis factor, with an HR of 2.80 (95% CI, 1.71-4.58, P < 0.001). The signature could identify patients who are suitable candidates for adjuvant chemotherapy.

CONCLUSIONS

An eight-CpG DNA methylation signature is a reliable prognostic and predictive tool for disease recurrence in patients with stage II CRC.

A novel nomogram for predicting overall survival in peripheral T cell lymphoma patients.. [DOI: 10.21203/rs.3.rs-2823604/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background The prognosis of peripheral T cell lymphomas (PTCLs) varies greatly. This study aimed at generating a prognostic nomogram based on differentially expressed genes (DEGs).Methods Firstly, we collected RNA transcripts from Gene Expression Omnibus and identified DEGs. Secondly we used univariate Cox regression, Least absolute shrinkage and selection operator (LASSO) to screen the independent risk factors to construct nomogram in the training cohort. Thirdly, we evaluate its prediction accuracy via decision curves analysis (DCA), receiver operating characteristic (ROC) and calibration rate to confirm its performance on survival in training and validation cohort. Then we carried out subgroup analysis in training and validation to eliminate the effects of age, gender, and pathological subtype. Lastly, to verify feasibility of nomogram in practice, we applied immunohistochemistry to clinical samples and analyzed the relationship between IHC scores and prognosis.Results The 702 DEGs between 40 PTCLs and 20 non-tumor patients were identified. Then ANGPTL2, CPSF4, CLIC4 and OTUD6B were screened out as independent risk factors via univariate Cox regression and LASSO. The DCA, ROC, Harrell’s concordance index (c-index) and calibration rate showed nomogram predicting more accurately than any single specific transcript. The results showed PTCLs with higher nomogram-score had a longer survival, regardless of age, gender and pathological subtype. Finally, the high expression level of ANGPTL2, CPSF4 and OTUD6B related to poor prognosis. Higher expression of CLIC4 related to longer survival.Conclusion This nomogram showed the favorable clinical applicability, regardless of age, gender and pathological subtype.

A Novel ypTLM Staging System Based on LODDS for Gastric Cancer After Neoadjuvant Therapy: Multicenter and Large-Sample Retrospective Study

BACKGROUND

The accuracy of the eighth AJCC ypTNM staging system on the prognosis of gastric cancer (GC) patients after neoadjuvant therapy (NAT) is controversial. This study aimed to develop and validate a novel staging system using the log odds of positive lymph nodes scheme (LODDS).

METHODS

A retrospective analysis of 606 GC patients who underwent radical gastrectomy after neoadjuvant therapy was conducted as the development cohort. (Fujian Medical University Affiliated Union Hospital (n = 183), Qinghai University Affiliated Hospital (n = 169), Mayo Clinic (n = 236), Lanzhou University First Hospital (n = 18)). The validation cohort came from the SEER database (n = 1701). A novel ypTLoddsS (ypTLM) staging system was established using the 3-year overall survival. The predictive performance of two systems was compared.

RESULTS

Two-step multivariate Cox regression analysis in both cohorts showed that ypTLM was an independent predictor of overall survival of GC patients after neoadjuvant therapy (HR: 1.57, 95% CI: 1.30-1.88, p < 0.001). In the development cohort, ypTLM had better discrimination ability than ypTNM (C-index: 0.663 vs 0.633, p < 0.001), better prediction homogeneity (LR: 97.7 vs. 70.9), and better prediction accuracy (BIC: 3067.01 vs 3093.82; NRI: 0.36). In the validation cohort, ypTLM had a better prognostic predictive ability (C-index: 0.614 vs 0.588, p < 0.001; LR: 11,909.05 vs. 11,975.75; BIC: 13,263.71 vs 13,328.24; NRI: 0.22). The time-dependent ROC curve shows that the predictive performance of ypTLM is better than ypTNM, and the analysis of the decision curve shows that ypTLM achieved better net benefits.

CONCLUSION

A LODDS-based ypTLM staging system based on multicenter data was established and validated. The predictive performance was superior to the eighth AJCC ypTNM staging system.

A new strategy for searching determinants in colorectal cancer progression through whole-part relationship combined with multi-omics

The high incidence and mortality of colorectal cancer (CRC) and the lack of adequate diagnostic molecules have led to poor treatment outcomes for colorectal cancer, making it particularly important to develop methods to obtain molecular with significant diagnostic effects. Here, we proposed a whole and part study strategy (early-stage colorectal cancer as "part" and colorectal cancer as "whole") to identify specific and co-pathways of change in early-stage and colorectal cancers and to discover the determinants of colorectal cancer development. Metabolite biomarkers discovered in plasma may not necessarily reflect the pathological status of tumor tissue. To explore the determinant biomarkers associated with plasma and tumor tissue in the CRC progression, multi-omics were performed on three phases of biomarker discovery studies (discovery, identification and validation) including 128 plasma metabolomes and 84 tissue transcriptomes. Importantly, we observe that the metabolic levels of oleic acid and FA (18:2) in patients with colorectal cancer were much higher than in healthy people. Finally, biofunctional verification confirmed that oleic acid and FA (18:2) can promote the growth of colorectal cancer tumor cells and be used as plasma biomarkers for early-stage colorectal cancer. We propose a novel research strategy to discover co-pathways and important biomarkers that may be targeted for a potential role in early colorectal cancer, and our work provides a promising tool for the clinical diagnosis of colorectal cancer.

High-yield areas to grade tumor budding in colorectal cancer: A practical approach for pathologists

BACKGROUND

Tumor budding (TB) has significant prognostic implication in stage II colorectal cancer (CRC) and is graded based on the International Tumor Budding Consensus Conference (ITBCC) protocol. In the current study, we evaluate tumor budding and its relationship to multiple histologic features in 104 tumors.

METHODS

One-hundred four resected CRC cases were retrieved. Tumor bud count and TB grade were compared to the final tumor bud count/TB grade of the tumor per ITBCC protocol. The following high-yield co-features were assessed in each slide: highest T stage, presence of benign mucosa, presence of a precursor lesion, and highest tumor volume.

RESULTS

Twenty-nine (28 %) cases had discrepancies between slide TB grade and final TB grade. The least discrepancies were seen in slides with benign mucosa (7 %) and precursor lesions (7 %). Among stage II patients without high-risk features, no discrepancies were observed in slides with benign mucosa. Slides with deepest invasion (r_s = 1.000, p = 0.01) and benign mucosa (r_s = 0.957, p < 0.001) had the strongest correlation with final tumor bud count in the same stage II subgroup. Similar relationships were observed when comparing final TB grade. Deepest invasion, tumor volume, as well as lymphovascular invasion, when present, also showed strong correlations with final TB grade in the entire cohort (r_s = 0.828-0.845, p < 0.001).

CONCLUSION

Our study is the first study to evaluate the relationship between TB grade and co-existing histologic features. We highlight the benefit of focusing on slides with high-yield co-features, with the strongest correlation seen in slides with adjacent benign mucosa and precursor lesions.

Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach

BACKGROUND

Early triage of patients with mushroom poisoning is essential for administering precise treatment and reducing mortality. To our knowledge, there has been no established method to triage patients with mushroom poisoning based on clinical data.

OBJECTIVE

The purpose of this work was to construct a triage system to identify patients with mushroom poisoning based on clinical indicators using several machine learning approaches and to assess the prediction accuracy of these strategies.

METHODS

In all, 567 patients were collected from 5 primary care hospitals and facilities in Enshi, Hubei Province, China, and divided into 2 groups; 322 patients from 2 hospitals were used as the training cohort, and 245 patients from 3 hospitals were used as the test cohort. Four machine learning algorithms were used to construct the triage model for patients with mushroom poisoning. Performance was assessed using the area under the receiver operating characteristic curve (AUC), decision curve, sensitivity, specificity, and other representative statistics. Feature contributions were evaluated using Shapley additive explanations.

RESULTS

Among several machine learning algorithms, extreme gradient boosting (XGBoost) showed the best discriminative ability in 5-fold cross-validation (AUC=0.83, 95% CI 0.77-0.90) and the test set (AUC=0.90, 95% CI 0.83-0.96). In the test set, the XGBoost model had a sensitivity of 0.93 (95% CI 0.81-0.99) and a specificity of 0.79 (95% CI 0.73-0.85), whereas the physicians' assessment had a sensitivity of 0.86 (95% CI 0.72-0.95) and a specificity of 0.66 (95% CI 0.59-0.73).

CONCLUSIONS

The 14-factor XGBoost model for the early triage of mushroom poisoning can rapidly and accurately identify critically ill patients and will possibly serve as an important basis for the selection of treatment options and referral of patients, potentially reducing patient mortality and improving clinical outcomes.

Association of NRF2 with HIF-2α-induced cancer stem cell phenotypes in chronic hypoxic condition

The acquisition of the cancer stem cell (CSC) properties is often mediated by the surrounding microenvironment, and tumor hypoxia is considered an important factor for CSC phenotype development. High levels of NRF2 (Nuclear Factor Erythroid 2-Like 2; NFE2L2), a transcription factor that maintains cellular redox balance, have been associated with facilitated tumor growth and therapy resistance. In this study, we investigated the role of NRF2 in hypoxia-induced CSC phenotypes in colorectal cancer cells. Chronic hypoxia for 72 h resulted in CSC phenotypes, including elevation of krupple-like factor 4 (KLF4) and octamer-binding transcription factor 4 (OCT4), and an increase in cancer migration and spheroid growth with concomitant hypoxia-inducible factor 2α (HIF-2α) accumulation. All these chronic hypoxia-induced CSC properties were attenuated following HIF-2α-specific silencing. In this chronic hypoxia model, NRF2 inhibition by shRNA-based silencing or brusatol treatment blocked HIF-2α accumulation, which consequently resulted in decreased CSC marker expression and inhibition of CSC properties such as spheroid growth. In contrast, NRF2 overactivation by genetic or chemical approach enhanced the chronic hypoxia-induced HIF-2α accumulation and cancer migration. As a molecular mechanism of the NRF2-inhibition-mediated HIF-2α dysregulation, we demonstrated that miR-181a-2-3p, whose expression is elevated in NRF2-silenced cells, targeted the HIF-2α 3'UTR and subsequently suppressed the chronic hypoxia-induced HIF-2α and CSC phenotypes. The miR-181a-2-3p inhibitor treatment in NRF2-silenced cells could restore the levels of HIF-2α and CSC markers, and increased cancer migration and sphere formation under chronic hypoxia. In line with this, the miR-181a-2-3p inhibitor transfection could increase tumorigenicity of NRF2-silenced colorectal cancer cells. Collectively, our study suggests the involvement of NRF2/miR181a-2-3p signaling in the development of HIF-2α-mediated CSC phenotypes in sustained hypoxic environments.

Development and validation of postoperative circulating tumor DNA combined with clinicopathological risk factors for recurrence prediction in patients with stage I-III colorectal cancer

BACKGROUND

Circulating tumor DNA (ctDNA) detection following curative-intent surgery could directly reflect the presence of minimal residual disease, the ultimate cause of clinical recurrence. However, ctDNA is not postoperatively detected in ≥ 50% of patients with stage I-III colorectal cancer (CRC) who ultimately recur. Herein we sought to improve recurrence risk prediction by combining ctDNA with clinicopathological risk factors in stage I-III CRC.

METHODS

Two independent cohorts, both consisting of early-stage CRC patients who underwent curative surgery, were included: (i) the discovery cohort (N = 124) with tumor tissues and postoperative plasmas for ctDNA determination; and (ii) the external validation cohort (N = 125) with available ctDNA results. In the discovery cohort, somatic variations in tumor tissues and plasmas were determined via a 733-gene and 127-gene next-generation sequencing panel, respectively.

RESULTS

In the discovery cohort, 17 of 108 (15.7%) patients had detectable ctDNA. ctDNA-positive patients had a significantly high recurrence rate (76.5% vs. 16.5%, P < 0.001) and short recurrence-free survival (RFS; P < 0.001) versus ctDNA-negative patients. In addition to ctDNA status, the univariate Cox model identified pathologic stage, lymphovascular invasion, nerve invasion, and preoperative carcinoembryonic antigen level associated with RFS. We combined the ctDNA and clinicopathological risk factors (CTCP) to construct a model for recurrence prediction. A significantly higher recurrence rate (64.7% vs. 8.1%, P < 0.001) and worse RFS (P < 0.001) were seen in the high-risk patients classified by the CTCP model versus those in the low-risk patients. Receiver operating characteristic analysis demonstrated that the CTCP model outperformed ctDNA alone at recurrence prediction, which increased the sensitivity of 2 year RFS from 49.6% by ctDNA alone to 87.5%. Harrell's concordance index, calibration curve, and decision curve analysis also suggested that the CTCP model had good discrimination, consistency, and clinical utility. These results were reproduced in the validation cohort.

CONCLUSION

Combining postoperative ctDNA and clinical risk may better predict recurrence than ctDNA alone for developing a personalized postoperative management strategy for CRC.

Prognostic and predictive value of a lncRNA signature in patients with stage II colon cancer

The current staging method is inadequate to identify high-risk recurrence patients with stage II colon cancer (CC). Using a systematic and comprehensive-biomarker discovery and validation method, we aimed to construct a lncRNA-based signature to improve the prognostic prediction of stage II CC. We identified 1,377 differently expressed lncRNAs by analyzing 16 paired stage II CC tumor tissue and adjacent normal mucosal tissue from the TCGA dataset. Subsequently, using a univariable and step multivariable Cox regression model, we trained an 11-lncRNA signature in the training cohort (n = 141), which could divide patients into high-risk and low-risk groups (AUC at 3 years = 0.801, 95% CI: 0.724-0.877; AUC at 5 years = 0.801, 95% CI: 0.718-0.885). Significantly, patients in the high-risk group had poorer recurrence-free survival (RFS) compared with the low-risk group (log-rank test, P < 0.001 in the training cohort). This lncRNA-based signature was further confirmed in the validation cohort (P < 0.001). Multivariate Cox regression and stratified survival analyses showed that the prognostic value of this signature was independent of other clinicopathological risk factors (CEA, T stage, and chemotherapy). Time-dependent receiver operating characteristic (ROC) analysis demonstrated that this signature had better prognostic ability than any other clinical risk factors or single lncRNAs (all P < 0.05). A nomogram was constructed for clinical use, which integrated both the lncRNA-based signature and clinical risk factors (CEA and T stage) and performed well in the calibration plots. Altogether, our lncRNA-based signature was an independent prognostic factor and possessed a stronger predictive power compared with the currently used clinicopathological risk factors when predicting the recurrence of patients with stage II CC. Collectively, this lncRNA-based signature might facilitate individualized treatment decisions and postoperative counseling, ultimately contributing to improved survival.

Incorporating higher order network structures to improve miRNA-disease association prediction based on functional modularity

As microRNAs (miRNAs) are involved in many essential biological processes, their abnormal expressions can serve as biomarkers and prognostic indicators to prevent the development of complex diseases, thus providing accurate early detection and prognostic evaluation. Although a number of computational methods have been proposed to predict miRNA-disease associations (MDAs) for further experimental verification, their performance is limited primarily by the inadequacy of exploiting lower order patterns characterizing known MDAs to identify missing ones from MDA networks. Hence, in this work, we present a novel prediction model, namely HiSCMDA, by incorporating higher order network structures for improved performance of MDA prediction. To this end, HiSCMDA first integrates miRNA similarity network, disease similarity network and MDA network to preserve the advantages of all these networks. After that, it identifies overlapping functional modules from the integrated network by predefining several higher order connectivity patterns of interest. Last, a path-based scoring function is designed to infer potential MDAs based on network paths across related functional modules. HiSCMDA yields the best performance across all datasets and evaluation metrics in the cross-validation and independent validation experiments. Furthermore, in the case studies, 49 and 50 out of the top 50 miRNAs, respectively, predicted for colon neoplasms and lung neoplasms have been validated by well-established databases. Experimental results show that rich higher order organizational structures exposed in the MDA network gain new insight into the MDA prediction based on higher order connectivity patterns.

DNA methylation profile in CpG-depleted regions uncovers a high-risk subtype of early-stage colorectal cancer

BACKGROUND

The current risk stratification system defined by clinicopathological features does not identify the risk of recurrence in early-stage (stage I-II) colorectal cancer (CRC) with sufficient accuracy. We aimed to investigate whether DNA methylation could serve as a novel biomarker for predicting prognosis in early-stage CRC patients.

METHODS

We analyzed the genome-wide methylation status of CpG loci using Infinium MethylationEPIC array run on primary tumor tissues and normal mucosa of early-stage CRC patients to identify potential methylation markers for prognosis. The machine-learning approach was applied to construct a DNA methylation-based prognostic classifier for early-stage CRC (MePEC) using the 4 gene methylation markers FAT3, KAZN, TLE4, and DUSP3. The prognostic value of the classifier was evaluated in 2 independent cohorts (n = 438 and 359, respectively).

RESULTS

The comprehensive analysis identified an epigenetic subtype with high risk of recurrence based on a group of CpG loci in the CpG-depleted region. In multivariable analysis, the MePEC classifier was independently and statistically significantly associated with time to recurrence in validation cohort 1 (hazard ratio = 2.35, 95% confidence interval = 1.47 to 3.76, P < .001) and cohort 2 (hazard ratio = 3.20, 95% confidence interval = 1.92 to 5.33, P < .001). All results were further confirmed after each cohort was stratified by clinicopathological variables and molecular subtypes.

CONCLUSIONS

We demonstrated the prognostic statistical significance of a DNA methylation profile in the CpG-depleted region, which may serve as a valuable source for tumor biomarkers. MePEC could identify an epigenetic subtype with high risk of recurrence and improve the prognostic accuracy of current clinical variables in early-stage CRC.

A novel prognostic model for patients with colon adenocarcinoma

BACKGROUND

Colon adenocarcinoma (COAD) is a highly heterogeneous disease, which makes its prognostic prediction challenging. The purpose of this study was to investigate the clinical epidemiological characteristics, prognostic factors, and survival outcomes of patients with COAD in order to establish and validate a predictive clinical model (nomogram) for these patients.

METHODS

Using the SEER (Surveillance, Epidemiology, and End Results) database, we identified patients diagnosed with COAD between 1983 and 2015. Disease-specific survival (DSS) and overall survival (OS) were assessed using the log-rank test and Kaplan-Meier approach. Univariate and multivariate analyses were performed using Cox regression, which identified the independent prognostic factors for OS and DSS. The nomograms constructed to predict OS were based on these independent prognostic factors. The predictive ability of the nomograms was assessed using receiver operating characteristic (ROC) curves and calibration plots, while accuracy was assessed using decision curve analysis (DCA). Clinical utility was evaluated with a clinical impact curve (CIC).

RESULTS

A total of 104,933 patients were identified to have COAD, including 31,479 women and 73,454 men. The follow-up study duration ranged from 22 to 88 months, with an average of 46 months. Multivariate Cox regression analysis revealed that age, gender, race, site_recode_ICD, grade, CS_tumor_size, CS_extension, and metastasis were independent prognostic factors. Nomograms were constructed to predict the probability of 1-, 3-, and 5-year OS and DSS. The concordance index (C-index) and calibration plots showed that the established nomograms had robust predictive ability. The clinical decision chart (from the DCA) and the clinical impact chart (from the CIC) showed good predictive accuracy and clinical utility.

CONCLUSION

In this study, a nomogram model for predicting the individualized survival probability of patients with COAD was constructed and validated. The nomograms of patients with COAD were accurate for predicting the 1-, 3-, and 5-year DSS. This study has great significance for clinical treatments. It also provides guidance for further prospective follow-up studies.

Identification and validation of a novel stress granules-related prognostic model in colorectal cancer

Aims: A growing body of evidence demonstrates that Stress granules (SGs), a non-membrane cytoplasmic compartments, are important to colorectal development and chemoresistance. However, the clinical and pathological significance of SGs in colorectal cancer (CRC) patients is unclear. The aim of this study is to propose a new prognostic model related to SGs for CRC on the basis of transcriptional expression. Main methods: Differentially expressed SGs-related genes (DESGGs) were identified in CRC patients from TCGA dataset by limma R package. The univariate and Multivariate Cox regression model was used to construct a SGs-related prognostic prediction gene signature (SGPPGS). The CIBERSORT algorithm was used to assess cellular immune components between the two different risk groups. The mRNA expression levels of the predictive signature from 3 partial response (PR) and 6 stable disease (SD) or progress disease (PD) after neoadjuvant therapy CRC patients' specimen were examined. Key findings: By screening and identification, SGPPGS comprised of four genes (CPT2, NRG1, GAP43, and CDKN2A) from DESGGs is established. Furthermore, we find that the risk score of SGPPGS is an independent prognostic factor to overall survival. Notably, the abundance of immune response inhibitory components in tumor tissues is upregulated in the group with a high-risk score of SGPPGS. Importantly, the risk score of SGPPGS is associated with the chemotherapy response in metastatic colorectal cancer. Significance: This study reveals the association between SGs related genes and CRC prognosis and provides a novel SGs related gene signature for CRC prognosis prediction.

Identification of the Minimum Combination of Serum microRNAs to Predict the Recurrence of Colorectal Cancer Cases

BACKGROUND

Serum microRNAs (miRNAs) have been recognized as potential stable biomarkers for various types of cancer. Considering the clinical applications, there are certain critical requirements, such as minimizing the number of miRNAs, reproducibility in a longitudinal clinical course, and superiority to conventional tumor markers, such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9. This study aimed to identify serum miRNAs that indicate the recurrence of colorectal cancer (CRC), surpassing inter-tumor heterogeneity.

METHODS

We conducted an analysis of 434 serum samples from 91 patients with CRC and 71 healthy subjects. miRNAs were obtained from Toray Co., Ltd, and miRNA profiles were analyzed using a three-step approach. miRNAs that were highly expressed in patients with CRC than in the healthy controls in the screening phase, and those that were highly expressed in the preoperative samples than in the 1-month postoperative samples in the discovery phase, were extracted. In the validation phase, the extracted miRNAs were evaluated in 323 perioperative samples, in chronological order.

RESULTS

A total of 12 miRNAs (miR-25-3p, miR-451a, miR-1246, miR-1268b, miR-2392, miR-4480, miR-4648, miR-4732-5p, miR-4736, miR-6131, miR-6776-5p, and miR-6851-5p) were significantly concordant with the clinical findings of tumor recurrence, however their ability to function as biomarkers was comparable with CEA. In contrast, the combination of miR-1246, miR-1268b, and miR-4648 demonstrated a higher area under the curve (AUC) than CEA. These three miRNAs were upregulated in primary CRC tissues.

CONCLUSION

We identified ideal combinatorial miRNAs to predict CRC recurrence.

Construction and validation of a prognostic model for gastrointestinal stromal tumors based on copy number alterations and clinicopathological characteristics

Background

The increasing incidence of gastrointestinal stromal tumors (GISTs) has led to the discovery of more novel prognostic markers. We aim to establish an unsupervised prognostic model for the early prediction of the prognosis of future patients with GISTs and to guide clinical treatment.

Methods

We downloaded the GISTs dataset through the cBioPortal website. We extracted clinical information and pathological information, including the microsatellite instability (MSI) score, fraction genome altered (FGA) score, tumor mutational burden (TMB), and copy number alteration burden (CNAB), of patients with GISTs. For survival analysis, we used univariate Cox regression to analyze the contribution of each factor to prognosis and calculated a hazard ratio (HR) and 95% confidence interval (95% CI). For clustering groupings, we used the t-distributed stochastic neighbor embedding (t-SNE) method for data dimensionality reduction. Subsequently, the k-means method was used for clustering analysis.

Results

A total of 395 individuals were included in the study. After dimensionality reduction with t-SNE, all patients were divided into two subgroups. Cluster 1 had worse OS than cluster 2 (HR=3.45, 95% CI, 2.22-5.56, P<0.001). The median MSI score of cluster 1 was 1.09, and the median MSI score of cluster 2 was 0.24, which were significantly different (P<0.001). The FGA score of cluster 1 was 0.28, which was higher than that of cluster 2 (P<0.001). In addition, both the TMB and CNAB of cluster 1 were higher than those of cluster 2, and the P values were less than 0.001.

Conclusion

Based on the CNA of GISTs, patients can be divided into high-risk and low-risk groups. The high-risk group had a higher MSI score, FGA score, TMB and CNAB than the low-risk group. In addition, we established a prognostic nomogram based on the CNA and clinicopathological characteristics of patients with GISTs.

Bta-miR-106b Regulates Bovine Mammary Epithelial Cell Proliferation, Cell Cycle, and Milk Protein Synthesis by Targeting the CDKN1A Gene

Our previous studies found that bta-miR-106b and its corresponding target gene, CDKN1A, were differentially expressed between the mammary epithelium of lactating Holstein cows with extremely high and low milk protein and fat percentage, implying the potential role of bta-miR-106b in milk composition synthesis. In this study, with luciferase assay experiment, bta-miR-106b was validated to target the 3'-untranslated region (UTR) of bovine CDKN1A, thereby regulating its expression. Moreover, in bovine mammary epithelial cells (BMECs), over-expression of bta-miR-106b significantly down-regulated the CDKN1A expression at both mRNA and protein levels, and inhibitors of bta-miR-106b increased CDKN1A expression. Of note, we observed that bta-miR-106b accelerated cell proliferation and cell cycle, and changed the expressions of protein synthesis related pathways such as JAK-STAT and PI3K/AKT/mTOR through regulating CDKN1A expression. Our findings highlight the important regulatory role of bta-miR-106b in milk protein synthesis by targeting CDKN1A in dairy cattle.

CT radiomics combined with clinical variables for predicting the overall survival of hepatocellular carcinoma patients after hepatectomy

PURPOSE

To establish a model for assessing the overall survival (OS) of the hepatocellular carcinoma (HCC) patients after hepatectomy based on the clinical and radiomics features.

METHODS

This study recruited a total of 267 patients with HCC, which were randomly divided into the training (N = 188) and validation (N = 79) cohorts. In the training cohort, radiomic features were selected with the intra-reader and inter-reader correlation coefficient (ICC), Spearman's correlation coefficient, and the least absolute shrinkage and selection operator (LASSO). The radiomics signatures were built by COX regression analysis and compared the predictive potential in the different phases (arterial, portal, and double-phase) and regions of interest (tumor, peritumor 3 mm, peritumor 5 mm). A clinical-radiomics model (CR model) was established by combining the radiomics signatures and clinical risk factors. The validation cohort was used to validate the proposed models.

RESULTS

A total of 267 patients 86 (45.74%) and 37 (46.84%) patients died in the training and validation cohorts, respectively. Among all the radiomics signatures, those based on the tumor and peritumor (5 mm) (AP-TP5-Signature) showed the best prognostic potential (training cohort 1-3 years AUC:0.774-0.837; validation cohort 1-3 years AUC:0.754-0.810). The CR model showed better discrimination, calibration, and clinical applicability as compared to the clinical model and radiomics features. In addition, the CR model could perform risk-stratification and also allowed for significant discrimination between the Kaplan-Meier curves in most of the subgroups.

CONCLUSIONS

The CR model could predict the OS of the HCC patients after hepatectomy.

Integrated Transcriptome Analysis Reveals mRNA-miRNA Pathway Crosstalk in Roman Laying Hens' Immune Organs Induced by AFB1

Aflatoxin B1 (AFB1) is a widely distributed contaminant in moldy corn, rice, soybean, and oil crops. Many studies have revealed its adverse effects, such as carcinogenicity, immunotoxicity, and hepatotoxicity, on the health of humans and animals. To investigate the immunotoxic effects on chicken immune organs induced by AFB1, we integrated RNA and small-RNA sequencing data of the spleen and the bursa of Fabricius to elucidate the response of the differentially expressed transcriptional profiles and related pathways. AFB1 consumption negatively influenced egg quality, but no obvious organ damage was observed compared to that of the control group. We identified 3918 upregulated and 2415 downregulated genes in the spleen and 231 upregulated and 65 downregulated genes in the bursa of Fabricius. We confirmed that several core genes related to immune and metabolic pathways were activated by AFB1. Furthermore, 42 and 19 differentially expressed miRNAs were found in the spleen and the bursa of Fabricius, respectively. Differentially expressed genes and target genes of differentially expressed miRNAs were mainly associated with cancer progression and immune response. The predicted mRNA-miRNA pathway network illustrated the potential regulatory mechanisms. The present study identified the transcriptional profiles and revealed potential mRNA-miRNA pathway crosstalk. This genetic regulatory network will facilitate the understanding of the immunotoxicity mechanisms of chicken immune organs induced by high concentrations of AFB1.

A novel chromatin regulator signature predicts the prognosis, clinical features and immunotherapy of colon cancer

Aim: To elucidate the potential function and prognostic value of chromatin regulators (CRs) in colon cancer. Materials & methods: A comprehensive analysis of CR RNA expression data from public databases was conducted. Results: The authors successfully established and validated a 17 CR-based signature using public databases. Ten CRs of the signature were eventually verified at the protein level using the Human Protein Atlas database. Functional enrichment showed that CRs were significantly enriched in cancer-related pathways. This signature was remarkably relevant to immune cell infiltration, immune checkpoints, tumor immune dysfunction and exclusion (TIDE) score and drug sensitivity. Conclusion: The authors identified a novel, reliable prognostic signature for colon cancer. The study provided new insights into the function of CRs and has important clinical implications for immunotherapy for colon cancer.